Discriminative training of GMM via log-likelihood ratio for abnormal acoustic event classification in vehicular environment

Kwangyoun Kim, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, a discriminative training technique based on Gaussian Mixture Model (GMM) is proposed for detection and classification of abnormal acoustic events in indoor environment. In particular, we consider small indoor space such as vehicular scenes and develop a two-step procedure in which statistical mapping of acoustic features is followed by abnormal event detection. In the first step, Mel-Frequency Cepstral Coefficients (MFCC) feature set is used to construct a Gaussian Mixture Model (GMM) for acoustic event mapping and log-likelihood ratio is used for confidence measure to correct misrecognition over vocal/nonvocal regions. In the 2nd step, an abnormal event is determined using maximum likelihood estimation approach wherein the ratio of abnormal events to cumulative events during an analysis window is compared to a threshold. For performance evaluation, we employ a statistically meaningful database of normal and abnormal acoustic events in actual indoor scenes of two representative scenarios. Subsequent experiments demonstrate a performance of 91% correct detection rate for abnormal context and 2.5% of error detection rate, which indicates it promising for real world vehicular acoustic surveillance applications.

Original languageEnglish
Title of host publicationProceedings - 1st ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, CNSI 2011
Pages348-352
Number of pages5
DOIs
Publication statusPublished - 2011
Event1st ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, CNSI 2011 - Jeju Island, Korea, Republic of
Duration: 2011 May 232011 May 25

Publication series

NameProceedings - 1st ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, CNSI 2011

Other

Other1st ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, CNSI 2011
CountryKorea, Republic of
CityJeju Island
Period11/5/2311/5/25

Keywords

  • Acoustic-based surveillance
  • Context awareness
  • GMM
  • MFCC

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering

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    Kim, K., & Ko, H. (2011). Discriminative training of GMM via log-likelihood ratio for abnormal acoustic event classification in vehicular environment. In Proceedings - 1st ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, CNSI 2011 (pp. 348-352). [5954340] (Proceedings - 1st ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, CNSI 2011). https://doi.org/10.1109/CNSI.2011.39